CN117826615B - Method for determining control parameters of cooling liquid of power battery of electric automobile - Google Patents
Method for determining control parameters of cooling liquid of power battery of electric automobile Download PDFInfo
- Publication number
- CN117826615B CN117826615B CN202410218035.6A CN202410218035A CN117826615B CN 117826615 B CN117826615 B CN 117826615B CN 202410218035 A CN202410218035 A CN 202410218035A CN 117826615 B CN117826615 B CN 117826615B
- Authority
- CN
- China
- Prior art keywords
- cooling liquid
- battery
- heat transfer
- cooling
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000000110 cooling liquid Substances 0.000 title claims abstract description 192
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012546 transfer Methods 0.000 claims abstract description 143
- 238000004088 simulation Methods 0.000 claims abstract description 110
- 230000004927 fusion Effects 0.000 claims abstract description 59
- 238000001816 cooling Methods 0.000 claims abstract description 44
- 238000012795 verification Methods 0.000 claims abstract description 21
- 238000013461 design Methods 0.000 claims abstract description 13
- 239000002826 coolant Substances 0.000 claims description 36
- 238000002474 experimental method Methods 0.000 claims description 27
- 230000020169 heat generation Effects 0.000 claims description 15
- 230000005855 radiation Effects 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- LYCAIKOWRPUZTN-UHFFFAOYSA-N Ethylene glycol Chemical compound OCCO LYCAIKOWRPUZTN-UHFFFAOYSA-N 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- WGCNASOHLSPBMP-UHFFFAOYSA-N hydroxyacetaldehyde Natural products OCC=O WGCNASOHLSPBMP-UHFFFAOYSA-N 0.000 description 2
- 238000013021 overheating Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000004781 supercooling Methods 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007710 freezing Methods 0.000 description 1
- 238000010921 in-depth analysis Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
Abstract
The invention discloses a method for determining control parameters of cooling liquid of an electric automobile power battery, and relates to the technical field of electric automobile thermal management. According to the method for determining the control parameters of the cooling liquid of the power battery of the electric automobile, the battery pack and the cooling liquid requirement are determined according to the design and the working condition of the power battery pack of the electric automobile, wherein the battery pack and the cooling liquid requirement comprise the battery thermal characteristic requirement, the cooling liquid heat transfer performance requirement and the cooling liquid cooling performance requirement, and further a battery thermal characteristic model and a cooling-heat transfer model are built, so that the thermal characteristics of the electric automobile in different working states and the heat transfer characteristics between the cooling liquid and the battery are respectively simulated; combining a battery thermal characteristic model and a cooling-heat transfer model to fuse and construct a multi-model fusion thermal simulation, and performing simulation on the battery pack; and further, experimental verification of the power battery of the actual electric automobile is carried out, a simulation compliance index is obtained, feasibility of multi-model fusion thermal simulation is verified, and the control parameters of the cooling liquid of the power battery of the electric automobile are optimized and adjusted.
Description
Technical Field
The invention relates to the technical field of electric automobile heat management, in particular to a method for determining control parameters of cooling liquid of a power battery of an electric automobile.
Background
Electric vehicle power cells typically have a relatively high power density, which means that they generate a significant amount of heat during operation, and effective thermal management systems are required to ensure stability and safety of the battery system under high power density conditions, where optimization of coolant control parameters is an important part.
With the continuous development of computer simulation and simulation technology, researchers can more accurately simulate the thermal behavior of a battery system under different working conditions, so that before actual manufacturing and testing, more in-depth analysis and optimization can be performed on control parameters of cooling liquid, for example, publication No.: the method for determining the cooling liquid control parameters of the electric automobile power battery disclosed by CN107145649A comprises the steps of correcting a simulation model of the electric automobile power battery by using a physical heat management rack system of the electric automobile power battery, and determining the cooling liquid control parameters of the electric automobile power battery under different factor levels by using the corrected simulation model and adopting an orthogonal test method. The embodiment of the invention adopts an orthogonal test method aiming at the combination of multiple factors and multiple levels to determine the optimal cooling liquid control parameters of the power battery of the electric automobile under the different factor levels, ensures the accuracy of simulation test results, reduces the test times, shortens the test period, accelerates the research and development progress of the electric automobile, reduces the research and development cost of the electric automobile, ensures the optimal cooling liquid control parameters under the aims of ensuring the low consumption level under the multiple factor levels, and provides a reliable test basis for the good control of the temperature of the power battery of the electric automobile and the reduction of the consumption.
However, the method for determining the coolant control parameters of the electric vehicle power battery lacks reliable data support, and the coolant control parameters of the electric vehicle power battery lacks classification research.
Therefore, in view of the above problems, there is a need for a method for determining the coolant control parameters of the power battery of an electric vehicle.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for determining the control parameters of the cooling liquid of the power battery of the electric automobile, which solves the problems of difficult determination of the control parameters of the cooling liquid of the power battery of the electric automobile, high experimental cost and different requirements for battery thermal management under different conditions.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the method for determining the control parameters of the cooling liquid of the power battery of the electric automobile comprises the following steps: constructing a battery thermal characteristic model and a cooling-heat transfer model which are respectively used for simulating the thermal characteristics of the electric automobile in different working states and simulating the heat transfer characteristics between the cooling liquid and the battery; combining a battery thermal characteristic model and a cooling-heat transfer model to fuse and construct a multi-model fusion thermal simulation, and performing simulation on the battery pack; according to the multi-model fusion thermal simulation result, experimental verification of the actual electric vehicle power battery is carried out, and a simulation compliance index is obtained and used for verifying the feasibility of multi-model fusion thermal simulation, so that the control parameters of the cooling liquid of the electric vehicle power battery are optimized and adjusted.
Further, a battery thermal characteristic model and a cooling-heat transfer model are constructed, and initial conditions include: and obtaining the type of cooling liquid of the power battery of the electric automobile according to the design and working conditions of the power battery pack of the electric automobile, and determining the battery pack and the cooling liquid requirement according to the type of cooling liquid, wherein the cooling liquid requirement comprises a battery thermal characteristic requirement, a cooling liquid heat transfer performance requirement and a cooling liquid cooling performance requirement.
Further, the specific simulation process of the battery thermal characteristic model is as follows: according to the battery thermal characteristic requirement, acquiring battery thermal characteristic control parameters, wherein the battery thermal characteristic control parameters comprise the internal heat generation rate, the internal heat conductivity and the battery thermal radiation safety value of the battery; and respectively giving weights to the internal heat generation rate, the internal heat conductivity and the thermal radiation safety value of the battery by combining with the analysis of the internal heat generation rate, the internal heat conductivity and the thermal radiation safety value of the battery, and carrying out weighted summation to obtain a battery thermal characteristic control index which is used for comprehensively evaluating the influence on the battery thermal characteristic when the battery thermal characteristic control parameters are different in value, wherein the battery thermal characteristic control index is not lower than a battery thermal characteristic control threshold.
Further, the specific simulation process of the cooling-heat transfer model is as follows: according to the cooling liquid heat transfer performance requirement and the cooling liquid cooling performance requirement, respectively obtaining a cooling liquid heat transfer control parameter and a cooling liquid cooling control parameter, wherein the cooling liquid heat transfer control parameter comprises: the cooling liquid heat transfer coefficient and the cooler heat transfer surface area, and the cooling liquid cooling control parameters comprise: cooling liquid flow rate and cooling liquid temperature; and respectively giving weights to the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature of the cooling liquid by combining with the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature of the cooling liquid, and carrying out weighted summation to obtain a cooling-heat transfer control index which is used for comprehensively evaluating the influence on the cooling-heat transfer performance of the cooling liquid of the power battery when the heat transfer control parameter of the cooling liquid and the cooling control parameter of the cooling liquid are different in value, wherein the cooling-heat transfer control index is not lower than a cooling-heat transfer control threshold.
Further, constructing a multi-model fusion thermal simulation, specifically comprising: performing a battery thermal characteristic control index debugging experiment through a battery thermal characteristic model to determine a preselected value range of a thermal characteristic control parameter model of the electric automobile power battery; performing a cooling-heat transfer control index debugging experiment through a cooling-heat transfer model to determine a preselected value range of a cooling liquid heat transfer control parameter model of the electric automobile power battery and a preselected value range of the cooling liquid heat transfer control parameter model; and respectively giving weights to the battery thermal characteristic control index and the cooling-heat transfer control index which are selected by debugging, and carrying out weighted summation to obtain a multi-model fusion thermal simulation control state index, wherein the multi-model fusion thermal simulation control state index is used for carrying out numerical display on the state change of the cooling liquid of the electric automobile power battery by combining the battery thermal characteristic model and the cooling-heat transfer model fusion.
Further, the multi-model fusion thermal simulation control state index has the following specific calculation formula: ; in the middle of Represented as a multi-model fusion thermal simulation control state index,Expressed as a battery thermal characteristic control index,Expressed as a cooling-to-heat transfer control index,、Respectively expressed as weight factors corresponding to the battery thermal characteristic control index and the cooling-heat transfer control index.
Further, the experimental verification of the actual electric automobile power battery is carried out, and the experimental verification specifically comprises the following steps: acquiring an experiment verification period; acquiring battery thermal characteristic control actual parameters, cooling liquid heat transfer control actual parameters and cooling liquid cooling control actual parameters of each group in an experimental verification period based on battery thermal characteristic requirements, cooling liquid heat transfer performance requirements and cooling liquid cooling performance requirements, respectively acquiring battery thermal characteristic control actual indexes and cooling-heat transfer control actual indexes, and further acquiring multi-model fusion actual control state indexes; and comparing and analyzing the multi-model fusion actual control state index and the multi-model fusion thermal simulation control state index to obtain a simulation compliance index, wherein the simulation compliance index is not lower than a simulation compliance threshold.
Further, the simulation accords with an index, and a specific calculation formula is as follows: ; in the middle of Represented as a simulated compliance index (plf),Represented as the experimental verification period of time,，Represented as a multi-model fusion thermal simulation control state index,Denoted as the firstThe multiple models verified by the secondary experiments fuse the actual control state indexes,Expressed as a natural constant.
Further, the optimizing adjustment of the control parameters of the cooling liquid of the power battery of the electric automobile specifically comprises: and according to the comparison analysis of the simulation coincidence index and the simulation coincidence threshold, selecting the actual parameters of the battery thermal characteristic control, the actual parameters of the cooling liquid heat transfer control and the actual parameters of the cooling liquid cooling control of each group of batteries when the simulation coincidence index is not lower than the simulation coincidence threshold as the cooling liquid control parameters of the power battery of the electric automobile.
Further, the method further comprises the following steps: and acquiring the cooling liquid control parameters of the power battery of the electric vehicle and the running state of the power battery of the electric vehicle in real time by utilizing a real-time monitoring function, alarming and disposing the identified overheat and fault conditions of the power battery of the electric vehicle by adopting an alarm mechanism, and correspondingly adjusting the cooling liquid control parameters of the power battery of the electric vehicle.
The invention has the following beneficial effects:
(1) According to the method for determining the control parameters of the cooling liquid of the power battery of the electric automobile, through definitely defining the thermal characteristics of the battery, the heat transfer performance of the cooling liquid and the cooling performance requirements, the power battery pack of the electric automobile can be better matched with the working conditions and the design specifications of the power battery pack of the electric automobile, and the stable temperature of the system in various working states is ensured; the battery thermal characteristic control parameters and the cooling liquid control parameters are evaluated, the influence of different parameter values on the system performance is calculated, parameter selection is optimized, and the efficiency and the stability of the system thermal management are improved; the multi-model fusion thermal simulation control state index is obtained by integrating the battery thermal characteristic control index and the cooling-heat transfer control index, and the index carries out numerical value display on the state change of the cooling liquid of the power battery of the electric automobile, so that the comprehensive understanding of the state change of the system is facilitated; the multi-model fusion actual control state index and the simulation control state index are compared and analyzed, so that the accuracy and the reliability of the simulation model can be evaluated, the confidence of an electric vehicle power battery cooling liquid system is enhanced, and the dependence of experimental verification is reduced; and according to the simulation conformity index, the control parameters of the cooling liquid of the power battery of the electric automobile are optimally adjusted to determine the optimal control parameter setting, so that the efficiency, performance and service life of the battery pack are improved, and the safety and reliability of the battery pack are improved.
(2) According to the method for determining the cooling liquid control parameters of the electric automobile power battery, the data processing technology is adopted, the cooling liquid control parameters of the electric automobile power battery are classified into the battery thermal characteristic control parameters, the cooling liquid heat transfer control parameters and the cooling liquid cooling control parameters, the battery thermal characteristic control index and the cooling-heat transfer control index are calculated by respectively utilizing weighted summation, and then the obtained multi-model fusion thermal simulation control state indexes are comprehensively obtained, so that the influence of the cooling liquid control parameters of the electric automobile power battery on the cooling liquid of the electric automobile power battery can be effectively displayed, the subjectivity of manual setting is avoided based on the control parameters determined based on the comparison of the simulation model and the actual experiment, the fault damage to the electric automobile power battery due to inaccuracy of the control parameters is avoided, and the usability of the electric automobile power battery is ensured.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
Fig. 1 is a flowchart of a method for determining a coolant control parameter of an electric vehicle power battery according to the present invention.
Detailed Description
According to the embodiment of the application, the cooling liquid control parameters are determined through simulation by the method for determining the cooling liquid control parameters of the power battery of the electric automobile, so that the experimental cost is reduced.
The problems in the embodiment of the application have the following general ideas: determining a battery pack and a cooling liquid requirement according to the design and working conditions of the electric automobile power battery pack, wherein the battery pack and the cooling liquid requirement comprise a battery thermal characteristic requirement, a cooling liquid heat transfer performance requirement and a cooling liquid cooling performance requirement; according to the battery thermal characteristic requirement, acquiring a battery thermal characteristic control parameter, calculating to obtain a battery thermal characteristic control index, and evaluating the influence on the battery thermal characteristic when the battery thermal characteristic control parameter is different in value; according to the heat transfer performance requirement of the cooling liquid and the cooling performance requirement of the cooling liquid, respectively obtaining a cooling liquid heat transfer control parameter and a cooling liquid cooling control parameter, calculating to obtain a cooling-heat transfer control index, and evaluating the influence on the cooling liquid cooling-heat transfer performance of the power battery when the cooling liquid heat transfer control parameter and the cooling liquid cooling control parameter are different in value; obtaining a multi-model fusion thermal simulation control state index through the battery thermal characteristic control index and the cooling-heat transfer control index, and carrying out numerical display on the state change of the cooling liquid of the power battery of the electric automobile; and further obtaining a multi-model fusion actual control state index obtained by an actual experiment, comparing and analyzing the multi-model fusion actual control state index with the multi-model fusion thermal simulation control state index to obtain a simulation compliance index, verifying the feasibility of multi-model fusion thermal simulation, optimizing and adjusting the cooling liquid control parameters of the power battery of the electric automobile, and determining the cooling liquid control parameters of the power battery of the electric automobile.
Referring to fig. 1, the embodiment of the invention provides a technical scheme: the method for determining the control parameters of the cooling liquid of the power battery of the electric automobile comprises the following steps: constructing a battery thermal characteristic model and a cooling-heat transfer model which are respectively used for simulating the thermal characteristics of the electric automobile in different working states and simulating the heat transfer characteristics between the cooling liquid and the battery; combining a battery thermal characteristic model and a cooling-heat transfer model to fuse and construct a multi-model fusion thermal simulation, and performing simulation on the battery pack; according to the multi-model fusion thermal simulation result, experimental verification of the actual electric vehicle power battery is carried out, simulation compliance indexes are obtained, feasibility of multi-model fusion thermal simulation is verified, and then optimization adjustment is carried out on cooling liquid control parameters of the electric vehicle power battery.
Specifically, a battery thermal characteristic model and a cooling-heat transfer model are constructed, and initial conditions include: according to the design and working conditions of the electric automobile power battery pack, the type of cooling liquid of the electric automobile power battery is obtained, and the battery pack and the cooling liquid requirement are determined according to the type of cooling liquid: battery thermal characteristics requirements, coolant heat transfer performance requirements, and coolant cooling performance requirements.
In this embodiment, to determine the type of coolant in the power battery of the electric vehicle, it is necessary to view the design specifications and technical documents of the battery pack, which include information about the thermal management requirements of the battery, the optimal operating temperature range, and recommended cooling system specifications, and also consider the operating conditions of the battery pack under different operating conditions, including the temperature range, the charge-discharge rate, the power requirements, and the like, consider various environments that the electric vehicle may face, such as extreme temperatures, high altitudes, different types of coolant, different cooling characteristics, and understand characteristics such as water-based coolant, glycol-based coolant, and the like: the water-based cooling liquid generally has better heat transfer performance, while the glycol-based cooling liquid can provide anti-freezing protection, but the heat transfer performance is possibly reduced, before the type of the cooling liquid is determined, the working conditions of the battery pack are simulated, the performance of different cooling liquid types in practical application is evaluated, and practical tests and verification are carried out to ensure that the selected cooling liquid type can meet the thermal management requirement of the battery pack, and the performance and service life of the battery are improved; determining battery pack and coolant requirements involves factors such as battery design, operating conditions, and coolant properties: the design specifications of the battery pack are known in detail by acquiring technical documents provided by battery manufacturers, which generally include information about the temperature range of the battery, the optimal operating conditions, the cooling system requirements, etc., taking into account various operating conditions that the battery pack may encounter in actual operation, including temperature range, charge-discharge rate, power requirements, using thermal performance simulation tools to perform tests, evaluating the performance of different coolant types under the operating conditions of the battery pack, helping to verify whether the selected coolant can effectively cool the battery, and meeting the system requirements.
Specifically, the specific simulation process of the battery thermal characteristic model is as follows: according to the battery thermal characteristic requirement, acquiring battery thermal characteristic control parameters, wherein the battery thermal characteristic control parameters comprise the internal heat generation rate, the internal heat conductivity and the battery thermal radiation safety value of the battery; the method comprises the steps of respectively giving weights to the internal heat generation rate of the battery, the internal heat conductivity of the battery and the thermal radiation safety value of the battery by combining with the analysis of the internal heat generation rate of the battery and the thermal radiation safety value of the battery, carrying out weighted summation to obtain a battery thermal characteristic control index, and comprehensively evaluating the influence on the battery thermal characteristic when the battery thermal characteristic control parameters are different in value, wherein the calculation formula is as follows: ; in the middle of Expressed as a battery thermal characteristic control index,Expressed as a battery thermal characteristic model simulation experiment period,，Denoted as the firstThe internal heat generation rate of the battery in the secondary experiment,Denoted as the firstThe internal thermal conductivity of the cell in the secondary experiment is shown as the firstThe battery thermal radiation safety value of the secondary experiment,、、Respectively expressed as an experiment preset value corresponding to the internal heat generation rate of the battery, the internal heat conductivity of the battery and the heat radiation safety value of the battery, and is set by a professional aiming at different performance of different types of cooling liquid,The battery thermal characteristic control index is not lower than the battery thermal characteristic control threshold, respectively expressed as a weight factor corresponding to the battery internal heat generation rate, the battery internal heat conductivity, and the battery thermal radiation safety value.
In this embodiment, the internal heat generation rate of the battery can be derived by measuring the heat generation condition of the battery pack under a specific operating condition using a heat measuring device such as a calorimeter or a thermal imager; the internal heat conductivity of the battery is obtained through measurement of a heat conductivity measuring instrument, such as a heat conductivity meter or a thermistor; the battery thermal radiation safety value is determined by thermal radiation experiments and simulation according to relevant standards and safety guidelines; meanwhile, the battery thermal characteristic control parameters can be obtained by inquiring related academic documents, technical manuals and data provided by battery manufacturers; the setting of the weighting factors corresponding to the internal heat generation rate, the internal heat conductivity and the thermal radiation safety value of the battery depends on the requirement setting of a specific battery thermal characteristic model simulation experiment, or the weighting factors are determined through discussions of related stakeholders and professional engineer teams so as to ensure that the trade-off of each performance meets the actual requirement.
Specifically, the specific simulation process of the cooling-heat transfer model is as follows: according to the cooling liquid heat transfer performance requirement and the cooling liquid cooling performance requirement, respectively obtaining a cooling liquid heat transfer control parameter and a cooling liquid cooling control parameter, wherein the cooling liquid heat transfer control parameter comprises: the cooling liquid heat transfer coefficient and the cooler heat transfer surface area, and the cooling liquid cooling control parameters comprise: cooling liquid flow rate and cooling liquid temperature; the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature analysis of the cooling liquid are combined, weights are respectively given to the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature of the cooling liquid, and the weighted summation is carried out to obtain a cooling-heat transfer control index which is used for comprehensively evaluating the influence on the cooling-heat transfer performance of the cooling liquid of the power battery when the heat transfer control parameter of the cooling liquid and the cooling control parameter of the cooling liquid are different in value, and the calculation formula is as follows: In the middle of Expressed as a cooling-to-heat transfer control index,Represented as a cool-heat transfer model simulation experiment period,，、Respectively denoted as the firstThe heat transfer control coefficient of the cooling liquid for secondary experimentThe cooling control coefficient of the cooling liquid in the secondary experiment,，、Respectively expressed as coolant heat transfer coefficient and cooler heat transfer surface area,，、Respectively expressed as a coolant flow rate and a coolant temperature,Respectively expressed as the experimental preset values corresponding to the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature of the cooling liquid, and is set by a professional for different performances of different types of cooling liquid,Respectively expressed as the weight factors corresponding to the heat transfer coefficient of the cooling liquid, the heat transfer surface area of the cooler, the flow rate of the cooling liquid and the temperature of the cooling liquid,、Respectively expressed as a weight factor corresponding to the coolant heat transfer control parameter and the coolant cooling control parameter, and the coolant-heat transfer control index is not lower than the coolant-heat transfer control threshold.
In the embodiment, the heat transfer coefficient of the cooling liquid is obtained by performing a thermal test in an actual system, measuring parameters such as temperature distribution, flow rate and the like of the cooling liquid, then calculating and obtaining by using a heat transfer equation, the heat transfer surface area of the cooler is obtained by measuring the actual geometric shape of the cooler, calculating the surface area, the flow rate of the cooling liquid is obtained by measuring the flow rate of the fluid through a flowmeter arranged on a fluid pipeline, and the temperature of the cooling liquid is obtained by directly measuring the temperature of the cooling liquid through a temperature sensor arranged in the cooling system; the coolant heat transfer coefficient, the chiller heat transfer surface area, the coolant flow rate, the weight factor corresponding to the coolant temperature, and the setting of the weight factor corresponding to the coolant heat transfer control parameter and the coolant cooling control parameter are determined depending on the modeling experiment requirements of the coolant-heat transfer model specific construction, in conjunction with cost effectiveness and engineering and design team discussion advice.
Specifically, a multi-model fusion thermal simulation is constructed, specifically comprising: performing a battery thermal characteristic control index debugging experiment through a battery thermal characteristic model to determine a preselected value range of a thermal characteristic control parameter model of the electric automobile power battery; performing a cooling-heat transfer control index debugging experiment through a cooling-heat transfer model to determine a preselected value range of a cooling liquid heat transfer control parameter model of the electric automobile power battery and a preselected value range of the cooling liquid heat transfer control parameter model; and analyzing the value range of the preselected value of the electric vehicle power battery thermal characteristic control parameter model, the value range of the preselected value of the cooling liquid heat transfer control parameter model and the value range of the preselected value of the cooling liquid cooling control parameter model, which are determined based on a debugging experiment, respectively giving weights to the battery thermal characteristic control index and the cooling-heat transfer control index selected in a debugging way, carrying out weighted summation to obtain a multi-model fusion thermal simulation control state index, and carrying out numerical display on the state change of the cooling liquid of the electric vehicle power battery by combining the battery thermal characteristic model and the cooling-heat transfer model fusion, wherein the multi-model fusion thermal simulation control state index is not lower than the multi-model fusion thermal simulation control state threshold.
In this embodiment, a battery thermal characteristic control index debugging experiment is performed through a battery thermal characteristic model to determine a preselected value range of a power battery thermal characteristic control parameter model, a series of experiments are required to be designed through the battery thermal characteristic model, and for each control index, the preselected value range of the control index is determined by adjusting the value thereof and observing the change of the model output; and (3) performing a cooling-heat transfer control index debugging experiment through a cooling-heat transfer model to determine a preselected value range of a cooling liquid heat transfer control parameter model of the electric automobile power battery and a preselected value range of the cooling liquid cooling control parameter model, wherein the values of the parameters are required to be adjusted through the cooling-heat transfer model, and observing the change of the model output to determine.
Specifically, the multi-model fusion thermal simulation control state index has the following specific calculation formula: ; in the middle of The multi-model fusion thermal simulation control state index is used for carrying out numerical display on the state change of the cooling liquid of the power battery of the electric automobile by combining a battery thermal characteristic model and a cooling-heat transfer model fusion,Expressed as a battery thermal characteristic control index,Expressed as a cooling-to-heat transfer control index,Respectively expressed as weight factors corresponding to the battery thermal characteristic control index and the cooling-heat transfer control index.
In this embodiment, the setting of the weight factors corresponding to the battery thermal characteristic control index and the cooling-heat transfer control index is determined by the goal and importance of the system performance, these weights are adjusted by a series of experiments or using an optimization algorithm, and the response of the system is observed by continuous adjustment, so as to find the weight allocation mode that optimizes the system performance; the calculation of the multi-model fusion thermal simulation control state index can provide more comprehensive and accurate information for the state change of the cooling liquid of the power battery of the electric automobile, and provide key technical support for sustainable development and popularization of the electric automobile.
Specifically, the experimental verification of the actual electric automobile power battery is carried out, and specifically comprises the following steps: acquiring an experiment verification period; acquiring each group of battery thermal characteristic control actual parameters, cooling liquid heat transfer control actual parameters and cooling liquid cooling control actual parameters in an experimental verification period based on battery thermal characteristic requirements, cooling liquid heat transfer performance requirements and cooling liquid cooling performance requirements, respectively acquiring a battery thermal characteristic control actual index and a cooling-heat transfer control actual index, further acquiring a multi-model fusion actual control state index, and obtaining a calculation formula and a multi-model fusion thermal simulation control state index, wherein each parameter is changed into each group of battery thermal characteristic control actual parameters, cooling liquid heat transfer control actual parameters and cooling liquid cooling control actual parameters in the experimental verification period; and comparing and analyzing the multi-model fusion actual control state index and the multi-model fusion thermal simulation control state index to obtain a simulation compliance index, wherein the simulation compliance index is not lower than a simulation compliance threshold.
In the embodiment, the models of thermal characteristic control, heat transfer control and cooling control can be verified and adjusted through each group of parameters acquired in the experimental verification period, the matching degree of the simulation model and an actual system is ensured, the accuracy and reliability of the model are enhanced, the simulation result is more reliable, the feasibility of multi-model fusion thermal simulation is verified through the comparative analysis of the multi-model fusion actual control state index and the thermal simulation control state index, and whether the multi-model fusion can effectively simulate and reflect the complex relationship of the thermal characteristic of the battery and the heat transfer control of the cooling liquid in actual application can be determined, so that the performance, the safety and the stability of the battery system of the electric automobile are improved.
Specifically, the simulation accords with the index, and a specific calculation formula is as follows: ; in the middle of Represented as a simulation compliance index, for verifying the feasibility of a multiple model fusion thermal simulation,Represented as the experimental verification period of time,Represented as a multi-model fusion thermal simulation control state index,Denoted as the firstThe multiple models verified by the secondary experiments fuse the actual control state indexes,Expressed as a natural constant.
In the embodiment, the consistency between the actual control state and the simulation control state can be compared by calculating the simulation compliance index, so that the accuracy of the multi-model fusion thermal simulation is evaluated, namely whether the simulation result can accurately reflect the dynamic change of the actual system, and important information is provided for system design, adjustment and optimization.
Specifically, the method for optimizing and adjusting the control parameters of the cooling liquid of the power battery of the electric automobile specifically comprises the following steps: and according to the comparison analysis of the simulation coincidence index and the simulation coincidence threshold, selecting the actual parameters of the battery thermal characteristic control, the actual parameters of the cooling liquid heat transfer control and the actual parameters of the cooling liquid cooling control of each group of batteries when the simulation coincidence index is not lower than the simulation coincidence threshold as the cooling liquid control parameters of the power battery of the electric automobile.
In this embodiment, the battery thermal characteristic control, the coolant heat transfer control and the coolant cooling control actual parameters when the simulation compliance index is not lower than the simulation compliance threshold are selected as the coolant control parameters of the electric vehicle power battery, which is conducive to improving the stability and safety of the system, reducing the risks of battery performance degradation, life shortening and even damage caused by overheating or supercooling, and by adjusting the coolant control parameters, the battery temperature can be better managed, thereby reducing the energy required for supercooling or overheating, being conducive to optimizing the energy utilization efficiency of the electric vehicle, prolonging the battery life and improving the overall performance.
Specifically, the method further comprises the following steps: and acquiring the cooling liquid control parameters of the power battery of the electric vehicle and the running state of the power battery of the electric vehicle in real time by utilizing a real-time monitoring function, alarming and disposing the identified overheat and fault conditions of the power battery of the electric vehicle by adopting an alarm mechanism, and correspondingly adjusting the cooling liquid control parameters of the power battery of the electric vehicle.
In this embodiment, through real-time supervision electric automobile power battery's coolant liquid control parameter and running state, can in time discover potential overheat and fault condition, help preventing that the problem from further worsening, protect battery system's safe and steady operation, help improving electric automobile power battery system's security, performance and life-span, reduce cost of maintenance simultaneously, provide more reliable driving experience for the user.
In summary, the present application has at least the following effects: the thermal characteristics of the power battery of the electric automobile under different working states can be more accurately simulated by constructing a battery thermal characteristic model and a cooling-heat transfer model; the battery thermal characteristic model and the cooling-heat transfer model are integrated into a comprehensive model, so that the simulation is more comprehensive, and the simulation accuracy and the simulation authenticity are improved; by analyzing the multi-model fusion thermal simulation results, the thermal management problem of the electric automobile power battery in different working states can be identified, and the adjustment and optimization of the control parameters of the cooling liquid are facilitated, so that the efficiency of a cooling system is improved, the temperature of the battery is reduced, and the performance and the service life of the battery are improved; the design can be iterated for many times before actual manufacture and test by using simulation, which is beneficial to accelerating the development period of products, improving the effectiveness of the design and meeting the thermal management requirements under different working conditions, thereby providing key support for the sustainable development of electric automobiles.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention is described with reference to flow charts of methods according to embodiments of the invention. It will be understood that each of the flows in the flowchart may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The method for determining the control parameters of the cooling liquid of the power battery of the electric automobile is characterized by comprising the following steps of:
constructing a battery thermal characteristic model and a cooling-heat transfer model which are respectively used for simulating the thermal characteristics of the electric automobile in different working states and simulating the heat transfer characteristics between the cooling liquid and the battery;
combining a battery thermal characteristic model and a cooling-heat transfer model to fuse and construct a multi-model fusion thermal simulation, and performing simulation on the battery pack;
According to the multi-model fusion thermal simulation result, carrying out experimental verification of the actual electric vehicle power battery to obtain a simulation compliance index, and verifying the feasibility of the multi-model fusion thermal simulation, so as to optimally adjust the control parameters of the cooling liquid of the electric vehicle power battery;
The battery thermal characteristic model and the cooling-heat transfer model are constructed, and initial conditions comprise: acquiring the type of cooling liquid of the power battery of the electric automobile according to the design and working conditions of the power battery pack of the electric automobile, and determining the battery pack and the cooling liquid requirement according to the type of cooling liquid, wherein the cooling liquid requirement comprises the battery thermal characteristic requirement, the cooling liquid heat transfer performance requirement and the cooling liquid cooling performance requirement;
the specific simulation process of the battery thermal characteristic model comprises the following steps:
according to the battery thermal characteristic requirement, acquiring battery thermal characteristic control parameters, wherein the battery thermal characteristic control parameters comprise the internal heat generation rate, the internal heat conductivity and the battery thermal radiation safety value of the battery;
the method comprises the steps of respectively giving weights to the internal heat generation rate of a battery, the internal heat conductivity of the battery and the thermal radiation safety value of the battery by combining with analysis of the internal heat generation rate of the battery and the thermal radiation safety value of the battery, carrying out weighted summation to obtain a battery thermal characteristic control index, and comprehensively evaluating the influence on the battery thermal characteristic when the battery thermal characteristic control parameters are different in value, wherein the battery thermal characteristic control index is not lower than a battery thermal characteristic control threshold;
The specific simulation process of the cooling-heat transfer model comprises the following steps:
According to the cooling liquid heat transfer performance requirement and the cooling liquid cooling performance requirement, respectively obtaining a cooling liquid heat transfer control parameter and a cooling liquid cooling control parameter, wherein the cooling liquid heat transfer control parameter comprises: the cooling liquid heat transfer coefficient and the cooler heat transfer surface area, and the cooling liquid cooling control parameters comprise: cooling liquid flow rate and cooling liquid temperature;
The method comprises the steps of combining a cooling liquid heat transfer coefficient, a cooler heat transfer surface area, a cooling liquid flow rate and a cooling liquid temperature for analysis, respectively giving weights to the cooling liquid heat transfer coefficient, the cooler heat transfer surface area, the cooling liquid flow rate and the cooling liquid temperature, and carrying out weighted summation to obtain a cooling-heat transfer control index which is used for comprehensively evaluating the influence on the cooling liquid cooling-heat transfer performance of the power battery when the cooling liquid heat transfer control parameter and the cooling liquid cooling control parameter are different in value, wherein the cooling-heat transfer control index is not lower than a cooling-heat transfer control threshold;
constructing a multi-model fusion thermal simulation, which specifically comprises the following steps:
Performing a battery thermal characteristic control index debugging experiment through a battery thermal characteristic model to determine a preselected value range of a thermal characteristic control parameter model of the electric automobile power battery;
performing a cooling-heat transfer control index debugging experiment through a cooling-heat transfer model to determine a preselected value range of a cooling liquid heat transfer control parameter model of the electric automobile power battery and a preselected value range of the cooling liquid heat transfer control parameter model;
and respectively giving weights to the battery thermal characteristic control index and the cooling-heat transfer control index which are selected by debugging, and carrying out weighted summation to obtain a multi-model fusion thermal simulation control state index, wherein the multi-model fusion thermal simulation control state index is used for carrying out numerical display on the state change of the cooling liquid of the electric automobile power battery by combining the battery thermal characteristic model and the cooling-heat transfer model fusion.
2. The method for determining a coolant control parameter of an electric vehicle power battery according to claim 1, wherein the multi-model fusion thermal simulation control state index has a specific calculation formula as follows:
；
In the middle of Represented as a multi-model fusion thermal simulation control state index,Expressed as a battery thermal characteristic control index,Expressed as a cooling-to-heat transfer control index,Respectively expressed as weight factors corresponding to the battery thermal characteristic control index and the cooling-heat transfer control index.
3. The method for determining the coolant control parameters of the power battery of the electric vehicle according to claim 1, wherein the experimental verification of the power battery of the actual electric vehicle is performed, specifically comprising:
acquiring an experiment verification period;
Acquiring battery thermal characteristic control actual parameters, cooling liquid heat transfer control actual parameters and cooling liquid cooling control actual parameters of each group in an experimental verification period based on battery thermal characteristic requirements, cooling liquid heat transfer performance requirements and cooling liquid cooling performance requirements, respectively acquiring battery thermal characteristic control actual indexes and cooling-heat transfer control actual indexes, and further acquiring multi-model fusion actual control state indexes;
And comparing and analyzing the multi-model fusion actual control state index and the multi-model fusion thermal simulation control state index to obtain a simulation compliance index, wherein the simulation compliance index is not lower than a simulation compliance threshold.
4. The method for determining a coolant control parameter of an electric vehicle power battery according to claim 3, wherein the simulation conforms to an index, and a specific calculation formula is as follows:
；
In the middle of Represented as a simulated compliance index (plf),Represented as the experimental verification period of time,，Represented as a multi-model fusion thermal simulation control state index,Denoted as the firstThe multiple models verified by the secondary experiments fuse the actual control state indexes,Expressed as a natural constant.
5. The method for determining the coolant control parameters of the electric vehicle power battery according to claim 4, wherein the optimizing and adjusting the coolant control parameters of the electric vehicle power battery specifically comprises: and according to the comparison analysis of the simulation coincidence index and the simulation coincidence threshold, selecting the actual parameters of the battery thermal characteristic control, the actual parameters of the cooling liquid heat transfer control and the actual parameters of the cooling liquid cooling control of each group of batteries when the simulation coincidence index is not lower than the simulation coincidence threshold as the cooling liquid control parameters of the power battery of the electric automobile.
6. The method for determining the coolant control parameter of the power battery of the electric vehicle according to claim 1, further comprising: and acquiring the cooling liquid control parameters of the power battery of the electric vehicle and the running state of the power battery of the electric vehicle in real time by utilizing a real-time monitoring function, alarming and disposing the identified overheat and fault conditions of the power battery of the electric vehicle by adopting an alarm mechanism, and correspondingly adjusting the cooling liquid control parameters of the power battery of the electric vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410218035.6A CN117826615B (en) | 2024-02-28 | Method for determining control parameters of cooling liquid of power battery of electric automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410218035.6A CN117826615B (en) | 2024-02-28 | Method for determining control parameters of cooling liquid of power battery of electric automobile |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117826615A CN117826615A (en) | 2024-04-05 |
| CN117826615B true CN117826615B (en) | 2024-07-02 |
Family
ID=
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108333528A (en) * | 2018-02-07 | 2018-07-27 | 重庆大学 | SOC and SOT united state methods of estimation based on power battery electric-thermal coupling model |
| CN112711879A (en) * | 2020-12-25 | 2021-04-27 | 中国第一汽车股份有限公司 | Three-dimensional thermal management simulation method for fuel cell engine |
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108333528A (en) * | 2018-02-07 | 2018-07-27 | 重庆大学 | SOC and SOT united state methods of estimation based on power battery electric-thermal coupling model |
| CN112711879A (en) * | 2020-12-25 | 2021-04-27 | 中国第一汽车股份有限公司 | Three-dimensional thermal management simulation method for fuel cell engine |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | A novel model-based fault detection method for temperature sensor using fractal correlation dimension | |
| US7366273B2 (en) | Method of determining margins to operating limits for nuclear reactor operation | |
| CN109614662B (en) | Method and system for determining heat dissipation mode of lithium battery pack in thermal simulation experiment | |
| CN112214862B (en) | Battery parameter calibration method, system and equipment based on genetic algorithm | |
| CN115267555A (en) | Battery SOH (State of health) evaluation system of energy storage system based on battery multipoint temperature measurement | |
| CN114692244A (en) | Lithium battery pack heat abuse safety risk assessment method based on multi-physical-field simulation | |
| CN110110367B (en) | Electrochemical energy storage cabinet thermal simulation method and system | |
| CN116108604A (en) | Water supply network abnormality detection method, system, equipment and storage medium | |
| CN117826615B (en) | Method for determining control parameters of cooling liquid of power battery of electric automobile | |
| Liu et al. | A framework for battery temperature estimation based on fractional electro-thermal coupling model | |
| CN117826615A (en) | Method for determining control parameters of cooling liquid of power battery of electric automobile | |
| CN113158589A (en) | Simulation model calibration method and device of battery management system | |
| CN117387978A (en) | Liquid cooling plate performance test and data processing method | |
| CN115389940A (en) | Method for predicting internal resistance of power battery, method and system for power, and storage medium | |
| Hu et al. | NARX modelling of a lithium iron phosphate battery used for electrified vehicle simulation | |
| CN114818377A (en) | Heat exchange equipment life loss detection method and device and electronic equipment | |
| CN113722926A (en) | Square lithium battery electric-thermal coupling modeling error source analysis method | |
| Kato et al. | 1d pde model for thermal dynamics in fluid-cooled battery packs: Numerical methods and sensor placement | |
| Xi et al. | Power mobile terminal security assessment based on weights self-learning | |
| CN104995624A (en) | Reliability design assistance device, reliability design assistance method, and reliability design assistance program | |
| Xiong et al. | Neural network and physical enable one sensor to estimate the temperature for all cells in the battery pack | |
| CN113516364B (en) | Method and device for stability assessment of high-proportion power electronic power system | |
| Wang et al. | Structure optimization of the battery thermal management system based on surrogate modeling of approximate and detailed simulations | |
| CN117728079B (en) | Battery temperature control management method and system for new energy battery pack | |
| CN117949835A (en) | Lithium cell chip test is with simulation charging system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |